Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation

Jiang, Yun; Yao, Huixia; Tao, Shengxin; Liang, Jian‐Gang

doi:10.3390/s21186177

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…The above operation is repeated three times and the final feature map size obtained in the encoding stage is one eighth of the original map size. Contrary to references [33][34][35], the plenary attention mechanism is not within the skip connections, but instead is at the bottleneck of the network. The spatial features in the meaningful channels are paid attention to by the PAM in the BottleNeck block of the network.…”

Section: Overall Network Architecturementioning

confidence: 75%

PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

Wang,

Wu,

Jia

2024

Applied Sciences

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Retinal vessel segmentation is critical for diagnosing related diseases in the medical field. However, the complex structure and variable size and shape of retinal vessels make segmentation challenging. To enhance feature extraction capabilities in existing algorithms, we propose PAM-UNet, a U-shaped network architecture incorporating a novel Plenary Attention Mechanism (PAM). In the BottleNeck stage of the network, PAM identifies key channels and embeds positional information, allowing spatial features within significant channels to receive more focus. We also propose a new regularization method, DropBlock_Diagonal, which discards diagonal regions of the feature map to prevent overfitting and enhance vessel feature learning. Within the decoder stage of the network, features from each stage are merged to enhance the segmentation accuracy of the final vessel. Experimental validation on two retinal image datasets, DRIVE and CHASE_DB1, shows that PAM-UNet achieves 97.15%, 83.16%, 98.45%, 83.15%, 98.66% and 97.64%, 85.82%, 98.46%, 82.56%, 98.95% on Acc, Se, Sp, F1, AUC, respectively, outperforming UNet and most other retinal vessel segmentation algorithms.

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Section: Overall Network Architecturementioning

confidence: 75%

PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

Wang,

Wu,

Jia

2024

Applied Sciences

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“…In the past decade, accompanied by the rapid development of deep learning, convolutional neural networks (CNNs) have also been widely carried out in the field of image segmentation, including retinal vessel segmentation. Compared with retinal vessel segmentation methods based on classical classifiers such as support vector machine (SVM) and k-nearest neighbor (KNN), the retinal vessel segmentation methods [ 4 , 5 , 6 ] based on convolutional neural network can more effectively extract image features from fundus images. Motivated by the great potential of convolutional neural networks on image segmentation, researchers did a lot of work in this area and proposed quite valuable structures, such as fully convolutional networks (FCNs) [ 7 ].…”

Section: Related Workmentioning

confidence: 99%

CPMF-Net: Multi-Feature Network Based on Collaborative Patches for Retinal Vessel Segmentation

Tang¹,

Deng²,

Yin³

2022

Sensors

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As an important basis of clinical diagnosis, the morphology of retinal vessels is very useful for the early diagnosis of some eye diseases. In recent years, with the rapid development of deep learning technology, automatic segmentation methods based on it have made considerable progresses in the field of retinal blood vessel segmentation. However, due to the complexity of vessel structure and the poor quality of some images, retinal vessel segmentation, especially the segmentation of Capillaries, is still a challenging task. In this work, we propose a new retinal blood vessel segmentation method, called multi-feature segmentation, based on collaborative patches. First, we design a new collaborative patch training method which effectively compensates for the pixel information loss in the patch extraction through information transmission between collaborative patches. Additionally, the collaborative patch training strategy can simultaneously have the characteristics of low occupancy, easy structure and high accuracy. Then, we design a multi-feature network to gather a variety of information features. The hierarchical network structure, together with the integration of the adaptive coordinate attention module and the gated self-attention module, enables these rich information features to be used for segmentation. Finally, we evaluate the proposed method on two public datasets, namely DRIVE and STARE, and compare the results of our method with those of other nine advanced methods. The results show that our method outperforms other existing methods.

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“…Convolutional operations extract local features from an image by extracting local characteristics from neighboring pixels [9]. A variety of segmentation models have been developed based on CNNs such as Fully Convolutional Networks (FCNs) [10], UNet [11], UNet 3+ [12], and DeepLab [13], among others [14][15][16][17][18][19][20][21][22][23][24]. UNet is one of the earliest and most-widely used techniques in medical image segmentation developed by Ronneberger et al [11] based on an encoder-decoder architecture [12].…”

Section: Introductionmentioning

confidence: 99%

Improved UNet with Attention for Medical Image Segmentation

AL Qurri,

Almekkawy

2023

Sensors

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Medical image segmentation is crucial for medical image processing and the development of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks (CNNs) have been widely adopted for medical image segmentation and have achieved significant success. UNet, which is based on CNNs, is the mainstream method used for medical image segmentation. However, its performance suffers owing to its inability to capture long-range dependencies. Transformers were initially designed for Natural Language Processing (NLP), and sequence-to-sequence applications have demonstrated the ability to capture long-range dependencies. However, their abilities to acquire local information are limited. Hybrid architectures of CNNs and Transformer, such as TransUNet, have been proposed to benefit from Transformer’s long-range dependencies and CNNs’ low-level details. Nevertheless, automatic medical image segmentation remains a challenging task due to factors such as blurred boundaries, the low-contrast tissue environment, and in the context of ultrasound, issues like speckle noise and attenuation. In this paper, we propose a new model that combines the strengths of both CNNs and Transformer, with network architectural improvements designed to enrich the feature representation captured by the skip connections and the decoder. To this end, we devised a new attention module called Three-Level Attention (TLA). This module is composed of an Attention Gate (AG), channel attention, and spatial normalization mechanism. The AG preserves structural information, whereas channel attention helps to model the interdependencies between channels. Spatial normalization employs the spatial coefficient of the Transformer to improve spatial attention akin to TransNorm. To further improve the skip connection and reduce the semantic gap, skip connections between the encoder and decoder were redesigned in a manner similar to that of the UNet++ dense connection. Moreover, deep supervision using a side-output channel was introduced, analogous to BASNet, which was originally used for saliency predictions. Two datasets from different modalities, a CT scan dataset and an ultrasound dataset, were used to evaluate the proposed UNet architecture. The experimental results showed that our model consistently improved the prediction performance of the UNet across different datasets.

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Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation

Cited by 6 publications

References 46 publications

PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

CPMF-Net: Multi-Feature Network Based on Collaborative Patches for Retinal Vessel Segmentation

Improved UNet with Attention for Medical Image Segmentation

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